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Minimum energy state

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

An annealing state in which neither improving nor worsening moves are accepted.

A minimum energy state is the stable end state of annealing in which neither improving nor worsening moves are accepted. In physical materials, aligned molecules form this low-energy structure; in simulated annealing, the objective-function minimum represents it. section 5.2.1 section 5.2.4

From cooled glass to optimization

Slowly cooling a material lets molecules align into a crystalline structure. Simulated annealing borrows this analogy: gradual cooling shifts the search from exploration toward exploitation and can avoid suboptimal local minima. The process can stop when the temperature is suitably low or when the system reaches its frozen minimum-energy state. section 5.2.1 section 5.2.4

Table 5.1 The physical annealing and simulated annealing analogy (view table figure)

Physical annealing

Simulated annealing

State of a material

Solution of an optimization problem

The energy of a state

The cost of a solution

Temperature

Control parameter (temperature)

High temperature makes molecules move freely

High temperature favors search space exploration

Low temperature restricts molecules’ motion

Low temperature leads to exploiting the search space

Gradual cooling helps to reduce stress and increase homogeneity and structural stability.

Gradual cooling helps to avoid getting stuck in suboptimal local minima and to find the globally optimal or near-optimal solution.

Use the physical-to-simulated annealing analogy to connect material state, energy, temperature, solution cost, exploration, and exploitation. section 5.2.1

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

How deep it goes
Explains the physical analogy and stopping condition but does not develop acceptance probabilities or implementation details.
Examples
Uses molecular alignment in crystalline solids, cooled glass, and optimization solutions as concrete examples.
What it assumes
Assumes familiarity with physical annealing and introduces the correspondence between material energy and optimization cost.
What you can do after
Evaluate when simulated annealing can stop because further improving or worsening moves are no longer accepted.
How it connects
Connects minimum energy state with simulated annealing, objective functions, cooling schedules, exploration, exploitation, and local minima.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Optimization Algorithms: AI techniques for design, planning, and control problems. The section links point to the parts of the book it draws on.